Data Availability Statement
Draft and audit data availability statements and repository plans for journal submission.
Installation
- Make sure Claude is on your device and in your terminal.
Skills load from
~/.claude/skills/when Claude Code starts up — so you need it on your machine first. If you don't have it yet, install it once with the command below, then runclaudein any terminal to verify.One-time setupnpm i -g @anthropic-ai/claude-codeAlready have it? Skip ahead.
- Paste into Claude Code or into your terminal.
This copies the whole skill folder into
~/.claude/skills/data-availability-boom5426/— the SKILL.md plus any scripts, reference docs, or templates the skill ships with. Safe default: works for every skill.Faster alternative (instruction-only skills)
Skips the clone and grabs only the SKILL.md file. Don't use this if the skill ships Python scripts, reference markdowns, or asset templates — they won't be downloaded and the skill will fail when it tries to load them.
Quick install (SKILL.md only)Sign up to copy - Restart Claude Code.
Quit and reopen Claude Code (or any other agent that loads from
~/.claude/skills/). New skills are picked up on startup. - Just ask Claude.
Skills auto-activate when your request matches the skill's description — no slash command needed. Trigger phrases live in the skill's own frontmatter; you can read them in the “What this skill does” section above.
Prefer to read the source first? Open on GitHub.
When Claude uses it
Use when drafting, auditing, or revising Data Availability statements, repository plans, accession-number placement, source-data coverage, or restricted-data wording for journal submission or resubmission.
What this skill does
Data Availability
Overview
Use this skill to turn manuscript-supporting data into a submission-ready availability package: statement text, repository plan, source-data mapping, and unresolved-risk flags.
This skill is narrower than submission-audit and more operational than scientific-writing. Use it when the bottleneck is no longer the paper's story, but whether the data-sharing package is specific, durable, and journal-ready.
When To Use
Use this skill when:
- the manuscript needs a
Data Availabilitystatement - accession numbers, DOIs, repository records, or source-data files are missing or unstable
- the paper mixes generated data, reused public data, and restricted data
- "available upon request" wording needs to be tightened or replaced
- a submission or revision needs data-sharing text that editors can audit quickly
Do not use this skill for:
- generic section rewriting unrelated to data sharing
- full manuscript restructuring
- bibliography cleanup that does not affect dataset availability
Workflow
- Identify the target journal and article type.
- Inventory the datasets that support the main and supplementary claims:
- raw data
- processed data
- figure source data
- reused public datasets
- code-adjacent outputs that are necessary to inspect the results
- Assign each dataset one access route only:
- public repository
- controlled-access repository
- within paper or supplement
- reused public source
- third-party restricted
- justified request route
- Choose repository and identifier strategy before drafting the statement.
- Draft explicit dataset-to-location wording.
- Check that the statement covers both newly generated and reused data.
- Flag unresolved fields rather than inventing repository names, accession IDs, DOIs, or access rules.
Working Rules
- Prefer public, discipline-specific repositories when possible.
- Treat
available upon requestas weak unless a real legal, ethical, commercial, or third-party restriction exists. - If data cannot be public, state:
- why access is restricted
- who controls access
- how requests are reviewed
- what metadata, source data, or derived data remain public
- Do not merge data, code, protocols, and materials into one vague availability sentence unless the journal explicitly wants that.
- Do not invent accession numbers, DOIs, repository names, reviewer links, or embargo terms.
Related Files
Open these only when needed:
references/statement-patterns.mdUse when drafting or repairing the actual statement text.references/repository-routing.mdUse when deciding where each dataset should live and what identifier type is needed.references/source-data-checks.mdUse when checking whether figures, tables, and supplements expose enough underlying data.references/fair-metadata-checklist.mdUse when the journal expects FAIR or DataCite-style dataset metadata, or a formal dataset citation.references/chinese-author-alignment.mdUse when aligning a Chinese-language data-availability draft to English submission wording.
Output Standard
When using this skill, return:
- ready-to-paste
Data Availabilitytext - repository and identifier actions still required
- missing information or blocking risks
- a short note on whether the current package is:
ready_to_submitdraft_with_placeholdersneeds_author_inputblocked
Related skills
DOCX Document Editor
Prat011
Create, edit, and analyze Word documents with tracked changes and comments.
Audit Reproducibility
pedrohcgs
Verify numeric claims in manuscripts match actual analysis outputs within tolerance.
Environment Snapshot
pedrohcgs
Capture your project's exact language versions, packages, and dependencies for reproducible research.
Deep Research
shobcoder
Research complex topics thoroughly with verified sources and structured findings.